The performance of DNN models developed based on sparse multi-channel sEMG is still significantly lower than that of High-Density sEMG (HD-sEMG) systems which has a high number of densely located electrodes to significantly increase the information rate [1]}, [2]}, [3]}. For instance, in Reference [2]}, the HGR accuracy of \(99.7\%\)  is reported using HD-sEMG, which is reduced to \(84.4\%\)  when sparse multi-channel sEMG signals are used. In this context, we aim to design a novel DNN architecture using sparse multi-channel sEMG signals provided by the \(\text{Ninapro}\)  [5]}, [6]} database, which is one of the most widely accepted sparse multi-channel sEMG benchmark datasets. We designed the novel \(\text{Temporal Convolutions}\) -Hand Gesture Recognition architecture (\(\text{TC-HGR}\) ) to reduce computational burden while maintaining high accuracy, which is of paramount importance to translate the classification results into smooth actions.
